Mats Heimdahl

M.S. Computer Science and Engineering from the Royal Institute of Technology, Sweden, 1988.

Ph.D. Computer Science, University of California at Irvine, 1994.

Biography:

Professor Mats Heimdahl specializes in software engineering and safety critical systems. He is the director of the University of Minnesota Software Engineering Center (UMSEC).

Heimdahl is the recipient of the National Science Foundation's CAREER award, a McKnight Land-Grant Professorship and the McKnight Presidential Fellow award at the University of Minnesota, and the University of Minnesota Award for Outstanding Contributions to Post-Baccalaureate, Graduate, and Professional Education.

Research:

Software is increasingly involved in our lives; software controls physical systems ranging from microwave ovens and watches to nuclear power plants, aircraft, and cars. Computer-related failures can, in many of these applications, have catastrophic effects.

My research group, the Critical Systems Research Group (CriSys), is conducting research in software engineering and is investigating methods and tools to help us develop software with predictable behavior free from defects.

Research in this area spans all aspects of system development ranging from concept formation and requirements specification, through design and implementation, to testing and maintenance. In particular, we are currently investigating model-based software development for critical systems.

Specifically, we are focusing on how to use various static verification techniques to assure that software requirements models possess desirable properties, how to correctly generate production code from software requirements models, how to validate models, and how to effectively use the models in the testing process.

Interests:

Software engineering and safety critical systems.

Recent Publications

The choice of test oracle—the artifact that determines whether an application under test executes correctly—can significantly impact the effectiveness of the testing process. However, despite the prevalence of tools that support test input selection, little work exists for supporting oracle creation. We propose a method of supporting test oracle creation that automatically selects the oracle data—the set of variables monitored during testing—for expected value test oracles.

Structural coverage metrics have been widely used to measure test suite adequacy as well as to generate test cases. In previous investigations, we have found that the fault-finding effectiveness of tests satisfying structural coverage criteria is highly dependent on program syntax – even if the faulty code is exercised, its effect may not be observable at the output. To address these problems, observability-based coverage metrics have been defined.

When evaluating assurance cases, being able to capture the confidence one has in the individual evidence nodes is crucial, as these values form the foundation for determining the confidence one has in the assurance case as a whole. Human opinions are subjective, oftentimes with uncertainty---it is difficult to capture an opinion with a single probability value. Thus, we believe that a distribution best captures a human opinion such as confidence.